The present invention relates to medical imaging of the heart, and more particularly, to automatic detection of coronary arteries in medical images of the heart.
According to the statistics from the United States Center for Disease Control and Prevention, cardiovascular disease (CVD) is a leading cause of death in the United States. As reported by the American Heart Association, coronary artery disease (CAD) causes a largest percentage among various types of CVDs. CAD is often caused by the narrowing of the coronary artery or atherosclerosis. This can lead to coronary artery stenosis and can cause heart attacks, angina pectoris, or both.
Various techniques exist to produce images of the heart, including computed tomography (CT), magnetic resonance imaging (MRI), fluoroscopic imaging, etc. These techniques can be utilized to produce large amounts of 2D image and 3D volume data that can be used for analyzing the heart. Segmentation of coronary arteries in such heart images is an important step for detection of plaques, aneurysms, stenoses, and other abnormalities of coronary arteries that can result in dysfunction or disease of the heart. However, coronary artery segmentation or detection can be a difficult problem due to large variation in shape, low contrast, calcification, stenosis, occlusion, bifurcation, and the existence of other vessel-like structures, such as veins.
Typical conventional techniques for segmenting coronary arteries utilize a tracking based method that tracks the coronary arteries starting from a point on the aorta. Such conventional techniques typically require a human input as a seed, and are semi-automatic approaches that may not be fast enough for clinical application. Furthermore, such conventional techniques are sensitive to artifacts, and a mistake caused by an artifact can lead to tracking of an entirely wrong path.